We evaluated retrieval effectiveness on five standard benchmark QA datasets (NQ, TriviaQA, WQ, CuratedTREC, SQuAD), exactly the same as [1]}.
We used the Retriever\(_{\textrm {Multi}}\)  model, which can be applied to all five datasets.
For end-to-end QA, we evaluated on NQ and TriviaQA with the available models.
More precisely, we used the Reader\(_{\textrm {NQ-Single}}\)  model to process the retrieved contexts from Retriever\(_{\textrm {NQ}}\)  for NQ and used the Reader\(_{\textrm {TQA-Multi}}\)  model to process the retrieved contexts from Retriever\(_{\textrm {Multi}}\)  for TriviaQA.
